Covid Report for Family

Background

Notes and format last updated May 7, 2020

Starting on the May 7th update, the NY Times began including probable covid cases/deaths along with confirmed. This mostly affects death counts – for certain geographies that include probable COVID deaths in addition to confirmed, these are now added to the totals. For the time being, they were all added to the May 6th totals, causing a big spike at the U.S. level. Over time, NY Times will revise their historical counts and distribute these added deaths when they actually occurred, so the spike should fade.

• This page shows COVID data broken down into regions that affect our family. It pulls from publicly available raw data from the NY Times, which is reported daily. It’s on a one-day lag, so when you see this report, the most recent day included will be that reported the day prior.
• The table of contents above summarizes the major sections and can be used to jump to those sections.
• Each major section is then divided into tabs to allow you to more easily shuffle between geographies and metrics.
  • If you open on your phone, I don’t believe the tabs will work, so you’ll just see the page as one long, continuous document.
• More detailed descriptions and context can be found in each section.

Growth rates

Heat maps

• The two heat maps below compare how quickly total cases or deaths have grown at various times in our respective geopgraphies.
• The first plot compares growth rate for total cases; the second, growth rate for total deaths.
• The metric used is doubling time, by which I mean how quickly total cases or deaths are doubling.
• The plots track that doubling time at each date for our geographies. Darker colors reflect shorter doubling times, and thus periods of faster growth.
  • You can use the plots to track each geography over time and to compare the geographies to one another.
  • You can also compare the cases and death charts, to see how faster periods of death growth follow faster periods of case growth.

Case growth rates

• This section charts the growth rate of both total and new cases for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total cases and new cases.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of cases, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new cases) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new case reporting at the state level.
  • For total cases, the trendlines are a rolling 3-day average of daily growth rates in total cases. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total cases have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total case line get as close to zero as possible.
  • For new cases, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new cases. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total cases, we want to watch for the lines for new cases to get consistently below zero and stay there. That means that we are consistently seeing fewer new cases on a daily basis.

U.S.

Our states

Death growth rates

• This section charts the growth rate of both total and new deaths for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total deaths and new deaths.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of deaths, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new deaths) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new death reporting at the state level.
  • For total deaths, the trendlines are a rolling 3-day average of daily growth rates in total deaths. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total deaths have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total death line get as close to zero as possible.
  • For new deaths, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new deaths. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total deaths, we want to watch for the lines for new deaths to get consistently below zero and stay there. That means that we are consistently seeing fewer new deaths on a daily basis.

U.S.

Our states

By population rankings

This section tracks metrics for states and counties normalized for population (number of cases or deaths per million residents), and then compares these figures both for our geographies and the country overall.

States

• This section shows tables ranking all 50 states for per populations rates of total cases, new cases, total deaths, and new deaths.
• For each metric, in addition to the tables, the trends for the top states are plotted over time.
  • We only plot the top ten states for each metric so that the plots aren’t too crowded. But you can view the full 50-state rankings in the tables.

Total confirmed cases

Table of total confirmed cases per million residents (all 50 states)

Ranking	State	Cases Per Million
1	North Dakota	130,646
2	South Dakota	126,385
3	Rhode Island	117,729
4	Utah	114,973
5	Arizona	111,647
6	Tennessee	110,156
7	Oklahoma	106,397
8	Iowa	106,071
9	Wisconsin	105,500
10	Arkansas	105,174
11	Nebraska	103,525
12	Kansas	101,599
13	Alabama	99,979
14	South Carolina	98,868
15	Indiana	98,220
16	Mississippi	98,076
17	Idaho	95,324
18	Nevada	94,837
19	Illinois	93,309
20	Wyoming	93,282
21	Montana	92,968
22	Louisiana	91,835
23	Georgia	91,321
24	Texas	90,546
25	Kentucky	90,481
26	California	89,809
27	Delaware	87,809
28	Florida	87,795
29	New Mexico	87,647
30	New Jersey	87,296
31	Minnesota	85,273
32	Missouri	83,937
33	Massachusetts	83,262
34	New York	82,827
35	Ohio	82,127
36	North Carolina	81,442
37	Alaska	78,669
38	Connecticut	78,025
39	Colorado	74,023
40	West Virginia	72,751
41	Pennsylvania	72,306
42	Virginia	66,656
43	Michigan	64,202
44	Maryland	62,635
45	District of Columbia	56,596
46	New Hampshire	54,613
47	Washington	44,637
48	Puerto Rico	41,686
49	Oregon	36,528
50	Maine	32,658
51	Vermont	23,667
52	Hawaii	19,218

New confirmed cases

Table of new cases per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Cases Per Million
1	Rhode Island	494
2	Connecticut	475
3	New Jersey	337
4	New York	331
5	South Carolina	316
6	Texas	290
7	Georgia	279
8	Delaware	264
9	Florida	262
10	Alaska	260
11	Kentucky	245
12	North Carolina	240
13	Alabama	229
14	Kansas	226
15	Massachusetts	222
16	Iowa	213
17	Idaho	209
18	Arkansas	207
19	New Hampshire	207
20	Arizona	201
21	Louisiana	197
22	Pennsylvania	194
23	Utah	194
24	South Dakota	189
25	Virginia	188
26	Montana	182
27	Colorado	177
28	Ohio	177
29	Michigan	167
30	New Mexico	157
31	Nebraska	154
32	Vermont	146
33	California	144
34	West Virginia	142
35	Mississippi	141
36	District of Columbia	139
37	Oklahoma	139
38	Illinois	134
39	Washington	129
40	Indiana	124
41	Maryland	117
42	Nevada	117
43	Wisconsin	116
44	Missouri	114
45	North Dakota	111
46	Wyoming	111
47	Minnesota	106
48	Maine	99
49	Oregon	98
50	Puerto Rico	45
51	Hawaii	32
52	Tennessee	29

Total deaths

Table of total deaths per million residents (all 50 states)

Ranking	State	Deaths Per Million
1	New Jersey	2,593
2	New York	2,399
3	Rhode Island	2,347
4	Massachusetts	2,313
5	Mississippi	2,219
6	Arizona	2,156
7	Connecticut	2,130
8	South Dakota	2,107
9	Louisiana	2,049
10	Alabama	1,987
11	North Dakota	1,928
12	Pennsylvania	1,861
13	Indiana	1,851
14	Arkansas	1,785
15	Illinois	1,781
16	New Mexico	1,745
17	Iowa	1,716
18	Michigan	1,640
19	Tennessee	1,636
20	South Carolina	1,631
21	Kansas	1,621
22	Nevada	1,597
23	Georgia	1,557
24	Texas	1,484
25	Ohio	1,458
26	Delaware	1,439
27	District of Columbia	1,418
28	Florida	1,412
29	Missouri	1,354
30	California	1,289
31	Maryland	1,288
32	West Virginia	1,275
33	Montana	1,262
34	Wisconsin	1,191
35	Wyoming	1,159
36	Minnesota	1,154
37	Nebraska	1,126
38	Oklahoma	1,077
39	North Carolina	1,058
40	Kentucky	1,048
41	Colorado	1,043
42	Idaho	1,030
43	Virginia	914
44	New Hampshire	850
45	Washington	655
46	Puerto Rico	628
47	Utah	586
48	Oregon	522
49	Maine	503
50	Alaska	381
51	Vermont	322
52	Hawaii	305

New deaths

Table of new deaths per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Deaths Per Million
1	Rhode Island	34
2	Virginia	18
3	California	13
4	Kansas	12
5	Delaware	11
6	Alabama	10
7	Georgia	9
8	Arizona	8
9	Iowa	8
10	Missouri	8
11	Texas	8
12	Connecticut	6
13	Florida	6
14	Kentucky	6
15	New Jersey	6
16	Ohio	6
17	Oklahoma	6
18	Tennessee	6
19	Massachusetts	5
20	Mississippi	5
21	Nevada	5
22	New Mexico	5
23	New York	5
24	Wyoming	5
25	Indiana	4
26	Louisiana	4
27	Maine	4
28	Maryland	4
29	North Carolina	4
30	Pennsylvania	4
31	South Carolina	4
32	Washington	4
33	West Virginia	4
34	Arkansas	3
35	Oregon	3
36	Wisconsin	3
37	District of Columbia	2
38	Idaho	2
39	Illinois	2
40	Puerto Rico	2
41	Utah	2
42	Vermont	2
43	Colorado	1
44	Michigan	1
45	Montana	1
46	Nebraska	1
47	Alaska	0
48	Hawaii	0
49	Minnesota	0
50	New Hampshire	0
51	North Dakota	0
52	South Dakota	0

Counties

• This section focuses on the county level. It shows tables with our counties ranked by percentile of U.S. counties for per population rates of total cases and total deaths.
  • Each table also shows the top five counties in the country in addition to our counties, for added perspecive.
• In addition to the tables, our counties’ percentile for both total cases and total deaths are plotted over time.

Confirmed cases

Table showing total cases per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Cases Per Million	Raw Ranking	Percentile
Crowley	Colorado	323,874	1	99
Chattahoochee	Georgia	278,903	2	99
Bent	Colorado	263,403	3	99
Lincoln	Arkansas	240,709	4	99
Dewey	South Dakota	239,477	5	99
Davidson	Tennessee	128,339	227	92
Richland	South Carolina	98,240	1108	64
York	South Carolina	92,405	1371	56
Orange	California	81,921	1858	40
Pierce	Washington	42,072	2901	7

Our county percentiles over time

Deaths

Table showing total deaths per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Deaths Per Million	Raw Ranking	Percentile
Gove	Kansas	8,346	1	99
Jerauld	South Dakota	7,948	2	99
Emporia city	Virginia	7,108	3	99
Galax city	Virginia	6,932	4	99
Foard	Texas	6,926	5	99
Orange	California	1,212	1947	38
Davidson	Tennessee	1,212	1949	37
Richland	South Carolina	1,171	1994	36
York	South Carolina	1,153	2019	35
Pierce	Washington	599	2698	14

Our county percentiles over time

Raw counts

• This section shows the raw counts over time for the U.S., our states, and our counties for four categories:
  • Total confirmed cases: This is the cumulative total of cases reported.
  • New confirmed cases: This is the number of new cases reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new cases, to help better visualize the peak.
  • Total deaths: This is the cumulative total of deaths reported.
  • New deaths: This is the number of new deaths reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new deaths, to help better visualize the peak.

Total confirmed cases

U.S.

Our states

Our counties

New confirmed cases

U.S.

Our states

Our counties

Total deaths

U.S.

Our states

Our counties

New deaths

U.S.

Our states

Our counties

Stay-at-home comparisons

• This section is a work in progress. It’s exploring what effect stay-at-home orders have on spread. It’s inherentely messy because some states have localized orders but not orders for their full state and because states implemented the orders at different times.
  • The first plot shows a rolling 3-day average of new cases per million residents, averaged across 2 groups of states – those with a state-wide stay-at-home order in effect and those without. The size of the dots reflect the number of states in each group for each given day (so the dots are getting bigger over time for the group with orders, as more states implement those orders).
    • So far, this view doesn’t show if these orders are “working” or not. Instead, the biggest takeaway from the current plot is that the states that implemented orders are reacting to having worse spread, which is why the case rate is so much higher at the start for them. As a state decides it has a problem and implements an order, it joins the grouping of states with orders and drives the rate up for that group.
    • Over time (if the policies work), what we’d expect is that the line for the group with orders flattens or declines in relation to the no-order group. It also may become moot if all states issue orders.
    • As states start rescending their orders, I’ll put them back into the no-order group, so the no-order group should beging to expand again.
  • The second plot tracks progress of states by the number of days since orders have been in effect. It includes only those states with orders and shows their average new case growth rate (a rolling 3-day average of daily growth rates) based on how long it’s been since the order went into effect. Again, the size of the dots show how many states are included in each point – i.e., as you go further right, the dots are smaller because less states are that far past their order.
    • I’m working on how to add a baseline to this chart to compare to states without orders, but that’s challenging because there’s no way to fully align timing.